EMA analyses
Dani Cosme
2022-11-28
load packages
library(pacman)
pacman::p_load(tidyverse, brms, ggeffects, kableExtra, tidybayes, install = TRUE)define aesthetics
palette = c("#e64626", "#1985a1", "#4c5c68", "#FAC748")
plot_aes = theme_minimal() +
theme(legend.position = "top",
legend.text = element_text(size = 16),
text = element_text(size = 18, family = "Futura Medium"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(color = "black"),
axis.line = element_line(colour = "black"),
axis.ticks.y = element_blank())define functions
make_table = function(data) {
data %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("signal_count", "signal", term),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
select(term, `b [95% CI]`) %>%
knitr::kable(digits = 2)
}load and tidy data
- remove trials missing stimulus information
- the following participants had technical errors during all mindful attention trials: sub-MURIP012, sub-MURIP063, sub-MURIP078
- remove trials in which the mean signal intensity of the brain map is +/- 3 SDs from the mean
task data
task_data = read.csv("../data/task/cuereact_all062920.csv", stringsAsFactors = FALSE) %>%
filter(grepl("rating", trial_type)) %>%
select(pID, block_num, trial_num, trial_type, resp, rt, stim) %>%
mutate(trial_cond = ifelse(grepl("friend3|friend4", trial_type), "regulation",
ifelse(grepl("friend1|friend2", trial_type), "up-regulation",
ifelse(grepl("mindful", trial_type), "regulation",
ifelse(grepl("nonalc", trial_type), "non-alcohol reactivity",
ifelse(grepl("rating_alc_react", trial_type), "reactivity", NA))))),
trial_cond = factor(trial_cond, levels = c("non-alcohol reactivity", "reactivity", "regulation", "up-regulation"))) %>%
rename("stimulus" = stim) %>%
filter(!is.na(stimulus))
task_conditions = task_data %>%
select(pID, trial_type) %>%
unique() %>%
filter(grepl("friend|mindful", trial_type) | pID %in% c("sub-MURIP012", "sub-MURIP063", "sub-MURIP078")) %>%
mutate(condition = ifelse(grepl("friend", trial_type), "perspective", "mindful attention")) %>%
select(-trial_type) %>%
unique()
task_data_all = task_data %>%
left_join(., task_conditions) %>%
mutate(condition = ifelse(is.na(condition), "control", condition),
trial_cond = factor(trial_cond, c("non-alcohol reactivity", "reactivity", "regulation", "up-regulation")))
task_mindful = task_data_all %>%
filter(condition == "mindful attention")signature expression data
file_dir = "../data/dots"
file_pattern = "sub.*"
file_list = list.files(file_dir, pattern = file_pattern)
dots_tmp = data.frame()
for (file in file_list) {
tmp = tryCatch(read.table(file.path(file_dir,file), fill = TRUE, header = TRUE, sep = ",") %>%
extract(file, c("pID", "beta"), ".*(sub-MURIC[0-9]{3}|sub-MURIP[0-9]{3})/(beta_[0-9]{4}).nii") %>%
mutate(stimulus = as.character(stimulus)), error = function(e) message(file))
dots_tmp = rbind(dots_tmp, tmp)
rm(tmp)
}
dots = dots_tmp %>%
left_join(., task_conditions) %>%
mutate(condition = ifelse(is.na(condition), "control", condition)) %>%
group_by(pID) %>%
filter(!stimulus == "nan") %>%
mutate(sd3_signal = 3 * sd(mean_signal, na.rm = TRUE),
grand_mean_signal = mean(mean_signal, na.rm = TRUE),
outlier = ifelse(mean_signal > grand_mean_signal + sd3_signal, 1,
ifelse(mean_signal < grand_mean_signal - sd3_signal, 1, 0))) %>%
select(-sd3_signal, -grand_mean_signal) %>%
filter(!outlier == 1)EMA data
ema = read.csv("../data/ema/cleaned_EMA.csv", stringsAsFactors = FALSE) %>%
select(pID, groupID, Condition, contains("signal"), Session.Name, active_week, drinks_number, contains("Craving"), gender_f, NumberResponses, SocialWeekend, amount_self_c, freq_self_c, contains("React")) %>%
mutate(craving_previous = lag(Craving_Alc),
Condition = gsub("mindful", "mindful attention", Condition),
pID = gsub("muric", "sub-MURIC", pID),
pID = gsub("muri", "sub-MURIP", pID),
active_week = gsub("active", "on", active_week),
active_week = gsub("control", "off", active_week),
time_of_day = ifelse(grepl("Morning", Session.Name), "morning",
ifelse(grepl("Evening", Session.Name), "evening", NA))) %>%
rename("condition" = Condition) merge and prep for modeling
merged = task_data_all %>%
full_join(., dots) %>%
filter(!pID == "sub-MURIC303") %>%
filter(trial_cond %in% c("reactivity", "regulation")) %>%
mutate(trial_cond_recode = ifelse(trial_cond == "regulation", .5, -.5),
trial_cond = as.factor(as.character(trial_cond)))
between = merged %>%
select(pID, dot, trial_cond, trial_cond_recode, condition) %>%
group_by(pID, trial_cond, trial_cond_recode, condition) %>%
summarize(dot_between = mean(dot, na.rm = TRUE)) %>%
group_by(trial_cond, condition) %>%
mutate(dot_between_c = scale(dot_between, scale = FALSE, center = TRUE)) %>%
mutate(sd_dot = sd(dot_between, na.rm = TRUE),
dot_between_std = dot_between_c / sd_dot) %>%
select(-sd_dot)
reg_react_expression = between %>%
select(pID, condition, trial_cond, dot_between_std) %>%
unique() %>%
mutate(trial_cond = sprintf("%s_expression", trial_cond)) %>%
spread(trial_cond, dot_between_std)
ratings = read.csv("../data/task/post_scan_ratings.csv", stringsAsFactors = FALSE) %>%
select(DistributionChannel, pID, mindful2) %>%
filter(DistributionChannel == "anonymous" & grepl("muri|MURI", pID)) %>%
extract(pID, c("pID", "number"), "(.*)([0-9]{3})") %>%
mutate(pID = ifelse(pID %in% c("muri", "MURI"), "MURIP", pID),
pID = sprintf("sub-%s%s", toupper(pID), number),
confidence_rating = scale(as.numeric(mindful2), center = TRUE, scale = TRUE)) %>%
select(-number, -mindful2, -DistributionChannel)
ema_within_intervention = ema %>%
left_join(., reg_react_expression) %>%
filter(!condition == "control") %>%
group_by(pID) %>%
mutate(drinks_number = scale(drinks_number, center = TRUE, scale = FALSE),
Alc_React_Mindful = scale(Alc_React_Mindful, center = TRUE, scale = FALSE),
Alc_React_Perspective = scale(Alc_React_Perspective, center = TRUE, scale = FALSE),
mindful_scale = scale(Alc_React_Mindful, center = TRUE, scale = TRUE),
perspective_scale = scale(Alc_React_Perspective, center = TRUE, scale = TRUE),
response_scale = ifelse(condition == "mindful attention", mindful_scale, perspective_scale),
craving_previous = scale(craving_previous, center = TRUE, scale = TRUE),
craving_scale = scale(Craving_Alc, center = TRUE, scale = TRUE))
ema_within = ema_within_intervention %>%
filter(condition == "mindful attention") %>%
left_join(., ratings)visualize raw data
intervention week effects
ema_within %>%
select(pID, drinks_number, mindful_scale, craving_scale, active_week) %>%
gather(key, value, -pID, -active_week) %>%
ggplot(aes(active_week, value)) +
stat_summary(aes(group = interaction(pID, key)), fun = "mean", geom = "line", color = "grey", size = .2) +
stat_summary(aes(group = key), fun = "mean", geom = "line") +
stat_summary(fun.data = "mean_cl_boot") +
facet_grid(~key) +
plot_aes +
theme(legend.position = "none")
correlations
mindful responses & drinking
Association between how mindful you were when you encountered alcohol and how much you drank (morning signal = reported evening drinking, evening signal = reporting morning/afternoon drinking)
ema_within %>%
ggplot(aes(mindful_scale, drinks_number, color = active_week, fill = active_week)) +
geom_point(size = .5, alpha = .4) +
geom_smooth(aes(group = interaction(pID, active_week)), method = "lm", se = FALSE, size = .2) +
geom_smooth(method = "lm", alpha = .2) +
scale_color_manual(values = palette) +
scale_fill_manual(values = palette) +
plot_aes
craving (lagged) & drinking
E.g. association between morning craving and morning/afternoon drinking reported in the evening
ema_within %>%
ggplot(aes(craving_previous, drinks_number, color = active_week, fill = active_week)) +
geom_point(size = .5, alpha = .4) +
geom_smooth(aes(group = interaction(pID, active_week)), method = "lm", se = FALSE, size = .2) +
geom_smooth(method = "lm", alpha = .2) +
scale_color_manual(values = palette) +
scale_fill_manual(values = palette) +
plot_aes
craving (lagged) & responses
E.g. association between morning craving and morning/afternoon mindful responses to alcohol reported in the evening
ema_within %>%
ggplot(aes(craving_previous, mindful_scale, color = active_week, fill = active_week)) +
geom_point(size = .5, alpha = .4) +
geom_smooth(aes(group = interaction(pID, active_week)), method = "lm", se = FALSE, size = .2) +
geom_smooth(method = "lm", alpha = .2) +
scale_color_manual(values = palette) +
scale_fill_manual(values = palette) +
plot_aes
responses & craving
E.g. association between morning/afternoon mindful responses to alcohol reported in the evening and craving reported in the evening
ema_within %>%
ggplot(aes(mindful_scale, craving_scale, color = active_week, fill = active_week)) +
geom_point(size = .5, alpha = .4) +
geom_smooth(aes(group = interaction(pID, active_week)), method = "lm", se = FALSE, size = .2) +
geom_smooth(method = "lm", alpha = .2) +
scale_color_manual(values = palette) +
scale_fill_manual(values = palette) +
plot_aes
exploratory analyses
prior = c(prior(normal(0, 1), class=b))mindful responses ~ active_week * regulation_expression
Do participants reporter more mindful responses (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who more strongly express the mindful attention signature?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(response_week, c("regulation_expression [vals]", "active_week")) %>%
data.frame()
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person mindful response rating (SD)\n", x = "\nsignature expression on mindful attention trials (SD)") +
plot_aes +
theme(legend.position = "top")
point range
predicted = ggeffects::ggpredict(response_week, c("regulation_expression [0, 1]", "active_week")) %>%
data.frame() %>%
mutate(x = ifelse(x == 1, "+1 SD", "mean"),
x = factor(x, levels = c("mean", "+1 SD")),
group = recode(group, "on" = "active", "off" = "control"),
group = factor(group, levels = c("control", "active")))
predicted %>%
ggplot(aes(group, predicted, color = x, group = x)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.05), size = 1.5) +
geom_line(position = position_dodge(0.05), size = 1.5) +
scale_color_manual(name = "mindful attention signature expression", values = palette) +
scale_x_discrete(expand = c(.1, .1)) +
coord_cartesian(ylim = c(-.5, .65)) +
labs(y = "within-person mindful response rating (SD)\n", x = "\nintervention week") +
plot_aes +
theme(legend.position = "top")
summary
make_table(response_week)| term | b [95% CI] |
|---|---|
| intercept | -0.49 [-0.72, -0.27] |
| intervention week (active) | 0.44 [0.18, 0.68] |
| signature expression | -0.10 [-0.26, 0.06] |
| signal | 0.01 [0.00, 0.02] |
| intervention week (active) x signature expression | 0.29 [0.04, 0.52] |
simple slopes
hypothesis(response_week,
c(expression_mean = "active_weekon = 0",
expression_1sd = "active_weekon + active_weekon:regulation_expression = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | 0.44 [0.18, 0.68] |
| expression_1sd | 0.73 [0.38, 1.04] |
craving ~ active_week * regulation_expression
Do participants reporter weaker cravings (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who more strongly express the mindful attention signature?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(craving_week, c("regulation_expression [vals]", "active_week")) %>%
data.frame()
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person craving rating (SD)\n", x = "\nsignature expression on mindful attention trials (SD)") +
plot_aes +
theme(legend.position = "top")
point range
predicted = ggeffects::ggpredict(craving_week, c("regulation_expression [0, 1]", "active_week")) %>%
data.frame() %>%
mutate(x = ifelse(x == 1, "+1 SD", "mean"),
x = factor(x, levels = c("mean", "+1 SD")),
group = recode(group, "on" = "active", "off" = "control"),
group = factor(group, levels = c("control", "active")))
predicted %>%
ggplot(aes(group, predicted, color = x, group = x)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.05), size = 1.5) +
geom_line(position = position_dodge(0.05), size = 1.5) +
scale_color_manual(name = "mindful attention signature expression", values = palette) +
scale_x_discrete(expand = c(.1, .1)) +
coord_cartesian(ylim = c(-.5, .65)) +
labs(y = "within-person craving rating (SD)\n", x = "\nintervention week") +
plot_aes +
theme(legend.position = "top")
summary
make_table(craving_week)| term | b [95% CI] |
|---|---|
| intercept | -0.10 [-0.20, 0.01] |
| intervention week (active) | 0.03 [-0.07, 0.13] |
| signature expression | 0.03 [-0.04, 0.10] |
| signal | 0.00 [-0.00, 0.01] |
| intervention week (active) x signature expression | -0.06 [-0.17, 0.04] |
simple slopes
hypothesis(craving_week,
c(expression_mean = "active_weekon = 0",
expression_1sd = "active_weekon + active_weekon:regulation_expression = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | 0.03 [-0.07, 0.13] |
| expression_1sd | -0.03 [-0.18, 0.11] |
drinking ~ active_week * regulation_expression
Do participants reporter drinking less (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who more strongly express the mindful attention signature?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(drinking_week, c("regulation_expression [vals]", "active_week")) %>%
data.frame()
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person number of drinks\n", x = "\nsignature expression on mindful attention trials (SD)") +
plot_aes +
theme(legend.position = "top")
point range
predicted = ggeffects::ggpredict(drinking_week, c("regulation_expression [0, 1]", "active_week")) %>%
data.frame() %>%
mutate(x = ifelse(x == 1, "+1 SD", "mean"),
x = factor(x, levels = c("mean", "+1 SD")),
group = recode(group, "on" = "active", "off" = "control"),
group = factor(group, levels = c("control", "active")))
predicted %>%
ggplot(aes(group, predicted, color = x, group = x)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.05), size = 1.5) +
geom_line(position = position_dodge(0.05), size = 1.5) +
scale_color_manual(name = "mindful attention signature expression", values = palette) +
scale_x_discrete(expand = c(.1, .1)) +
coord_cartesian(ylim = c(-.3, .3)) +
labs(y = "within-person number of drinks\n", x = "\nintervention week") +
plot_aes +
theme(legend.position = "top")
summary
make_table(drinking_week)| term | b [95% CI] |
|---|---|
| intercept | 0.20 [0.05, 0.35] |
| intervention week (active) | -0.06 [-0.22, 0.08] |
| signature expression | 0.11 [0.02, 0.21] |
| signal | -0.01 [-0.01, -0.00] |
| intervention week (active) x signature expression | -0.23 [-0.38, -0.08] |
simple slopes
hypothesis(drinking_week,
c(expression_mean = "active_weekon = 0",
expression_1sd = "active_weekon + active_weekon:regulation_expression = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | -0.06 [-0.22, 0.08] |
| expression_1sd | -0.29 [-0.50, -0.08] |
drinking ~ mindful responses * regulation_expression
Do participants reporter drinking less (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who more strongly express the mindful attention signature?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(drinking_mindful, c("mindful_scale [vals]", "regulation_expression [0, 1]")) %>%
data.frame() %>%
mutate(group = ifelse(group == 1, "+1 SD", "mean"),
group = factor(group, levels = c("mean", "+1 SD")),)
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person number of drinks\n", x = "\nwithin-person mindful response rating (SD)") +
plot_aes +
theme(legend.position = "top")
summary
make_table(drinking_mindful)| term | b [95% CI] |
|---|---|
| intercept | 1.34 [0.90, 1.80] |
| mindful_scale | -0.57 [-0.96, -0.14] |
| signature expression | -0.27 [-0.50, -0.05] |
| signal | -0.00 [-0.02, 0.02] |
| mindful_scale x signature expression | -0.32 [-0.72, 0.08] |
simple slopes
hypothesis(drinking_mindful,
c(expression_mean = "mindful_scale = 0",
expression_1sd = "mindful_scale + mindful_scale:regulation_expression = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | -0.57 [-0.96, -0.14] |
| expression_1sd | -0.88 [-1.43, -0.35] |
mediation
prior = c(prior(normal(0, 1), class=b))mindful responses: no moderation
fit_brm %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("mindful_scale", "mindful response", term),
response = gsub("mindfulscale", "mindful response", response),
response = gsub("drinksnumber", "number of drinks", response),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
rename("outcome" = response) %>%
select(outcome, term, `b [95% CI]`) %>%
arrange(outcome) %>%
knitr::kable(digits = 2)| outcome | term | b [95% CI] |
|---|---|---|
| mindful response | intercept | -0.24 [-0.39, -0.09] |
| mindful response | intervention week (active) | 0.50 [0.26, 0.73] |
| number of drinks | intercept | 1.33 [0.94, 1.74] |
| number of drinks | intervention week (active) | -0.02 [-0.56, 0.52] |
| number of drinks | mindful response | -0.59 [-0.93, -0.20] |
hypothesis(
fit_brm,
'b_drinksnumber_mindful_scale * b_mindfulscale_active_weekon + cor_pID__mindfulscale_active_weekon__drinksnumber_active_weekon * sd_pID__mindfulscale_active_weekon * sd_pID__drinksnumber_mindful_scale = 0',
class = NULL,
seed = 6523
)## Hypothesis Tests for class :
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (b_drinksnumber_m... = 0 -0.29 0.14 -0.56 -0.01 NA
## Post.Prob Star
## 1 NA *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.mindful responses: moderation
run model
#summary(fit_brm_m)
#prior_summary(fit_brm_m)
fit_brm_m %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("mindful_scale", "mindful response", term),
response = gsub("mindfulscale", "mindful response", response),
response = gsub("drinksnumber", "number of drinks", response),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
rename("outcome" = response) %>%
select(outcome, term, `b [95% CI]`) %>%
arrange(outcome) %>%
knitr::kable(digits = 2, format = "pandoc")| outcome | term | b [95% CI] |
|---|---|---|
| mindful response | intercept | -0.21 [-0.38, -0.05] |
| mindful response | intervention week (active) | 0.49 [0.26, 0.71] |
| mindful response | signature expression | -0.15 [-0.29, 0.00] |
| mindful response | intervention week (active) x signature expression | 0.42 [0.18, 0.62] |
| number of drinks | intercept | 1.30 [0.93, 1.71] |
| number of drinks | intervention week (active) | 0.12 [-0.48, 0.70] |
| number of drinks | signature expression | -0.30 [-0.58, -0.03] |
| number of drinks | mindful response | -0.59 [-0.97, -0.18] |
| number of drinks | signature expression x mindful response | -0.30 [-0.69, 0.05] |
hypothesis(
fit_brm_m,
'b_drinksnumber_mindful_scale * b_mindfulscale_active_weekon + cor_pID__mindfulscale_active_weekon__drinksnumber_active_weekon * sd_pID__mindfulscale_active_weekon * sd_pID__drinksnumber_mindful_scale = 0',
class = NULL,
seed = 6523
)## Hypothesis Tests for class :
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (b_drinksnumber_m... = 0 -0.26 0.13 -0.53 -0.03 NA
## Post.Prob Star
## 1 NA *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plots
mindful responses by intervention week
slopes_within = ema_within %>%
select(pID, active_week, mindful_scale) %>%
rename("x" = active_week,
"predicted" = mindful_scale) %>%
mutate(x = recode(x, "on" = "active", "off" = "control"),
x = factor(x, levels = c("control", "active"))) %>%
group_by(pID, x) %>%
summarize(predicted = mean(predicted, na.rm = TRUE))
predicted = ggeffects::ggpredict(fit_brm_m, terms = c("active_week", "regulation_expression [0, 1]")) %>%
data.frame() %>%
mutate(x = recode(x, "on" = "active", "off" = "control"),
group = recode(group, "0" = "mean", "1" = "+1 SD"),
x = factor(x, levels = c("control", "active")))
(plot_response = predicted %>%
filter(response.level == "mindfulscale") %>%
ggplot(aes(x, predicted)) +
geom_line(data = slopes_within, aes(x, predicted, group = pID), alpha = .3) +
geom_line(aes(group = group, color = group), size = 2, , position = position_dodge(.1)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = group), size = 2, position = position_dodge(.1)) +
scale_x_discrete(expand = c(.1, .1)) +
scale_color_manual(values = palette, name = "signature expression") +
labs(x = "\nintervention week", y = "within-person mindful response (SD)\n") +
plot_aes)
alchol consumption ~ mindful responses
vals = seq(-2,2,.2)
points_within = ema_within %>%
select(pID, drinks_number, mindful_scale) %>%
rename("x" = mindful_scale,
"predicted" = drinks_number)
predicted = ggeffects::ggpredict(fit_brm_m, terms = c("mindful_scale", "regulation_expression [0, 1]")) %>%
data.frame() %>%
mutate(group = recode(group, "0" = "mean", "1" = "+1 SD"))
(plot_alcohol = predicted %>%
filter(response.level == "drinksnumber") %>%
ggplot(aes(x, predicted)) +
geom_point(data = points_within, alpha = .2, size = 2, position = position_jitter(.1,.1)) +
geom_line(aes(group = group, color = group), size = 2) +
geom_ribbon(aes(fill = group, ymin = conf.low, ymax = conf.high), size = 2, alpha = .4) +
scale_color_manual(values = palette, name = "signature expression") +
scale_fill_manual(values = palette, name = "signature expression") +
labs(x = "\nwithin-person mindful response (SD)", y = "number of drinks\n") +
plot_aes)
combined
ggpubr::ggarrange(plot_response, plot_alcohol, nrow = 1, common.legend = TRUE)
craving: no moderation
summary(fit_brm_c)## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: craving_previous ~ active_week + (0 + active_week | i | pID)
## drinks_number ~ active_week + craving_previous + (0 + active_week + craving_previous | i | pID)
## Data: ema_within (Number of observations: 1740)
## Draws: 4 chains, each with iter = 500; warmup = 250; thin = 4;
## total post-warmup draws = 250
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate
## sd(cravingprevious_active_weekoff) 0.05
## sd(cravingprevious_active_weekon) 0.05
## sd(drinksnumber_active_weekoff) 0.11
## sd(drinksnumber_active_weekon) 0.11
## sd(drinksnumber_craving_previous) 0.31
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) -0.17
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) -0.06
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.01
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.04
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) -0.02
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.40
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) -0.20
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.18
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.06
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) -0.09
## Est.Error
## sd(cravingprevious_active_weekoff) 0.04
## sd(cravingprevious_active_weekon) 0.04
## sd(drinksnumber_active_weekoff) 0.06
## sd(drinksnumber_active_weekon) 0.06
## sd(drinksnumber_craving_previous) 0.05
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 0.41
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 0.40
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.39
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.40
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.39
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.41
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 0.37
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.38
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.33
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.34
## l-95% CI
## sd(cravingprevious_active_weekoff) 0.00
## sd(cravingprevious_active_weekon) 0.00
## sd(drinksnumber_active_weekoff) 0.01
## sd(drinksnumber_active_weekon) 0.01
## sd(drinksnumber_craving_previous) 0.22
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) -0.86
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) -0.77
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) -0.74
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) -0.71
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) -0.74
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.93
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) -0.82
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) -0.58
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) -0.59
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) -0.68
## u-95% CI Rhat
## sd(cravingprevious_active_weekoff) 0.14 1.00
## sd(cravingprevious_active_weekon) 0.14 1.00
## sd(drinksnumber_active_weekoff) 0.23 1.01
## sd(drinksnumber_active_weekon) 0.24 1.00
## sd(drinksnumber_craving_previous) 0.43 1.00
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 0.69 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 0.73 1.00
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.74 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.74 1.00
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.70 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.58 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 0.57 1.01
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.81 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.65 1.01
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.59 1.00
## Bulk_ESS
## sd(cravingprevious_active_weekoff) 834
## sd(cravingprevious_active_weekon) 951
## sd(drinksnumber_active_weekoff) 722
## sd(drinksnumber_active_weekon) 738
## sd(drinksnumber_craving_previous) 982
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 833
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 819
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 876
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 1022
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 939
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 807
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 376
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 412
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 723
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 729
## Tail_ESS
## sd(cravingprevious_active_weekoff) 861
## sd(cravingprevious_active_weekon) 905
## sd(drinksnumber_active_weekoff) 915
## sd(drinksnumber_active_weekon) 866
## sd(drinksnumber_craving_previous) 821
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 784
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 970
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 951
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 980
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 950
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 794
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 642
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 531
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 787
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 823
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## cravingprevious_Intercept -0.01 0.03 -0.08 0.06 1.00
## drinksnumber_Intercept 0.07 0.05 -0.02 0.16 1.00
## cravingprevious_active_weekon 0.03 0.05 -0.07 0.13 1.00
## drinksnumber_active_weekon -0.16 0.07 -0.30 -0.01 1.00
## drinksnumber_craving_previous 0.30 0.06 0.18 0.42 1.00
## Bulk_ESS Tail_ESS
## cravingprevious_Intercept 1013 1038
## drinksnumber_Intercept 1053 738
## cravingprevious_active_weekon 1143 979
## drinksnumber_active_weekon 1014 883
## drinksnumber_craving_previous 879 938
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_cravingprevious 1.00 0.02 0.97 1.03 1.00 952
## sigma_drinksnumber 1.26 0.02 1.22 1.31 1.00 799
## Tail_ESS
## sigma_cravingprevious 881
## sigma_drinksnumber 933
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).fit_brm_c %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("craving_previous", "craving rating", term),
response = gsub("cravingprevious", "craving rating", response),
response = gsub("drinksnumber", "number of drinks", response),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
rename("outcome" = response) %>%
select(outcome, term, `b [95% CI]`) %>%
arrange(outcome) %>%
knitr::kable(digits = 2)| outcome | term | b [95% CI] |
|---|---|---|
| craving rating | intercept | -0.01 [-0.08, 0.06] |
| craving rating | intervention week (active) | 0.03 [-0.07, 0.13] |
| number of drinks | intercept | 0.07 [-0.02, 0.16] |
| number of drinks | intervention week (active) | -0.16 [-0.30, -0.01] |
| number of drinks | craving rating | 0.30 [0.18, 0.42] |
hypothesis(
fit_brm_c,
'b_drinksnumber_craving_previous * b_cravingprevious_active_weekon + cor_pID__cravingprevious_active_weekon__drinksnumber_active_weekon * sd_pID__cravingprevious_active_weekon * sd_pID__drinksnumber_craving_previous = 0',
class = NULL,
seed = 6523
)## Hypothesis Tests for class :
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (b_drinksnumber_c... = 0 0.01 0.02 -0.03 0.05 NA
## Post.Prob Star
## 1 NA
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.craving: moderation
summary(fit_brm_c)## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: craving_previous ~ active_week * regulation_expression + (0 + active_week | i | pID)
## drinks_number ~ active_week + regulation_expression * craving_previous + (0 + active_week + craving_previous | i | pID)
## Data: ema_within (Number of observations: 1597)
## Draws: 4 chains, each with iter = 500; warmup = 250; thin = 4;
## total post-warmup draws = 250
##
## Group-Level Effects:
## ~pID (Number of levels: 34)
## Estimate
## sd(cravingprevious_active_weekoff) 0.06
## sd(cravingprevious_active_weekon) 0.05
## sd(drinksnumber_active_weekoff) 0.12
## sd(drinksnumber_active_weekon) 0.11
## sd(drinksnumber_craving_previous) 0.30
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) -0.17
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) -0.01
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) -0.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) -0.02
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.01
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.38
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) -0.22
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.17
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) -0.09
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.09
## Est.Error
## sd(cravingprevious_active_weekoff) 0.04
## sd(cravingprevious_active_weekon) 0.04
## sd(drinksnumber_active_weekoff) 0.07
## sd(drinksnumber_active_weekon) 0.07
## sd(drinksnumber_craving_previous) 0.05
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 0.41
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 0.41
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.41
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.40
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.40
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.39
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 0.38
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.38
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.35
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.36
## l-95% CI
## sd(cravingprevious_active_weekoff) 0.00
## sd(cravingprevious_active_weekon) 0.00
## sd(drinksnumber_active_weekoff) 0.01
## sd(drinksnumber_active_weekon) 0.01
## sd(drinksnumber_craving_previous) 0.21
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) -0.83
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) -0.75
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) -0.74
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) -0.73
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) -0.71
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.92
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) -0.83
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) -0.60
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) -0.72
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) -0.62
## u-95% CI Rhat
## sd(cravingprevious_active_weekoff) 0.16 1.00
## sd(cravingprevious_active_weekon) 0.15 1.00
## sd(drinksnumber_active_weekoff) 0.25 1.00
## sd(drinksnumber_active_weekon) 0.26 1.00
## sd(drinksnumber_craving_previous) 0.43 1.00
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 0.68 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 0.72 1.00
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.76 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.75 1.00
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.74 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.49 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 0.58 1.01
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.81 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.63 1.00
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.73 1.00
## Bulk_ESS
## sd(cravingprevious_active_weekoff) 748
## sd(cravingprevious_active_weekon) 969
## sd(drinksnumber_active_weekoff) 809
## sd(drinksnumber_active_weekon) 908
## sd(drinksnumber_craving_previous) 976
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 1010
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 878
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 1000
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 898
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 942
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 837
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 475
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 646
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 730
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 714
## Tail_ESS
## sd(cravingprevious_active_weekoff) 772
## sd(cravingprevious_active_weekon) 940
## sd(drinksnumber_active_weekoff) 913
## sd(drinksnumber_active_weekon) 1082
## sd(drinksnumber_craving_previous) 915
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 891
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 899
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 831
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 960
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 617
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 991
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 704
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 817
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 870
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 867
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## cravingprevious_Intercept -0.00 0.03 -0.07
## drinksnumber_Intercept 0.06 0.05 -0.04
## cravingprevious_active_weekon 0.01 0.05 -0.09
## cravingprevious_regulation_expression 0.02 0.04 -0.05
## cravingprevious_active_weekon:regulation_expression -0.03 0.05 -0.14
## drinksnumber_active_weekon -0.14 0.07 -0.28
## drinksnumber_regulation_expression -0.00 0.04 -0.07
## drinksnumber_craving_previous 0.29 0.06 0.18
## drinksnumber_regulation_expression:craving_previous 0.06 0.07 -0.08
## u-95% CI Rhat Bulk_ESS
## cravingprevious_Intercept 0.07 1.00 958
## drinksnumber_Intercept 0.17 1.00 915
## cravingprevious_active_weekon 0.12 1.00 915
## cravingprevious_regulation_expression 0.10 1.00 820
## cravingprevious_active_weekon:regulation_expression 0.07 1.00 1001
## drinksnumber_active_weekon 0.00 1.00 974
## drinksnumber_regulation_expression 0.07 1.00 971
## drinksnumber_craving_previous 0.41 1.00 997
## drinksnumber_regulation_expression:craving_previous 0.18 1.00 901
## Tail_ESS
## cravingprevious_Intercept 948
## drinksnumber_Intercept 1042
## cravingprevious_active_weekon 950
## cravingprevious_regulation_expression 729
## cravingprevious_active_weekon:regulation_expression 883
## drinksnumber_active_weekon 985
## drinksnumber_regulation_expression 828
## drinksnumber_craving_previous 897
## drinksnumber_regulation_expression:craving_previous 1037
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_cravingprevious 1.00 0.02 0.97 1.04 1.00 978
## sigma_drinksnumber 1.27 0.02 1.22 1.31 1.00 993
## Tail_ESS
## sigma_cravingprevious 839
## sigma_drinksnumber 882
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#prior_summary(fit_brm_m)
fit_brm_c %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("craving_previous", "craving rating", term),
response = gsub("cravingprevious", "craving rating", response),
response = gsub("drinksnumber", "number of drinks", response),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
rename("outcome" = response) %>%
select(outcome, term, `b [95% CI]`) %>%
arrange(outcome) %>%
knitr::kable(digits = 2)| outcome | term | b [95% CI] |
|---|---|---|
| craving rating | intercept | -0.00 [-0.07, 0.07] |
| craving rating | intervention week (active) | 0.01 [-0.09, 0.12] |
| craving rating | signature expression | 0.02 [-0.05, 0.10] |
| craving rating | intervention week (active) x signature expression | -0.03 [-0.14, 0.07] |
| number of drinks | intercept | 0.06 [-0.04, 0.17] |
| number of drinks | intervention week (active) | -0.14 [-0.28, 0.00] |
| number of drinks | signature expression | -0.00 [-0.07, 0.07] |
| number of drinks | craving rating | 0.29 [0.18, 0.41] |
| number of drinks | signature expression x craving rating | 0.06 [-0.08, 0.18] |
hypothesis(
fit_brm_c,
'b_drinksnumber_craving_previous * b_cravingprevious_active_weekon + cor_pID__cravingprevious_active_weekon__drinksnumber_active_weekon * sd_pID__cravingprevious_active_weekon * sd_pID__drinksnumber_craving_previous = 0',
class = NULL,
seed = 6523
)## Hypothesis Tests for class :
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (b_drinksnumber_c... = 0 0 0.02 -0.03 0.04 NA
## Post.Prob Star
## 1 NA
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.confidence ratings
mindful responses ~ active_week * confidence_rating
Do participants reporter more mindful responses (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who reported greater confidence with mindful attention?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(response_week, c("confidence_rating [vals]", "active_week")) %>%
data.frame()
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person mindful response rating (SD)\n", x = "\nconfidence rating (SD)") +
plot_aes +
theme(legend.position = "top")
point range
predicted = ggeffects::ggpredict(response_week, c("confidence_rating [0, 1]", "active_week")) %>%
data.frame() %>%
mutate(x = ifelse(x == 1, "+1 SD", "mean"),
x = factor(x, levels = c("mean", "+1 SD")),
group = recode(group, "on" = "active", "off" = "control"),
group = factor(group, levels = c("control", "active")))
predicted %>%
ggplot(aes(group, predicted, color = x, group = x)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.05), size = 1.5) +
geom_line(position = position_dodge(0.05), size = 1.5) +
scale_color_manual(name = "confidence rating", values = palette) +
scale_x_discrete(expand = c(.1, .1)) +
coord_cartesian(ylim = c(-.5, .65)) +
labs(y = "within-person mindful response rating (SD)\n", x = "\nintervention week") +
plot_aes +
theme(legend.position = "top")
summary
make_table(response_week)| term | b [95% CI] |
|---|---|
| intercept | -0.53 [-0.76, -0.30] |
| intervention week (active) | 0.45 [0.16, 0.72] |
| confidence_rating | -0.02 [-0.20, 0.16] |
| signal | 0.01 [0.01, 0.02] |
| intervention week (active) x confidence_rating | 0.07 [-0.22, 0.36] |
simple slopes
hypothesis(response_week,
c(expression_mean = "active_weekon = 0",
expression_1sd = "active_weekon + active_weekon:confidence_rating = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | 0.45 [0.16, 0.72] |
| expression_1sd | 0.53 [0.12, 0.91] |
craving ~ active_week * confidence_rating
Do participants reporter weaker cravings (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who reported greater confidence with mindful attention?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(craving_week, c("confidence_rating [vals]", "active_week")) %>%
data.frame()
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person craving rating (SD)\n", x = "\nconfidence rating (SD)") +
plot_aes +
theme(legend.position = "top")
point range
predicted = ggeffects::ggpredict(craving_week, c("confidence_rating [0, 1]", "active_week")) %>%
data.frame() %>%
mutate(x = ifelse(x == 1, "+1 SD", "mean"),
x = factor(x, levels = c("mean", "+1 SD")),
group = recode(group, "on" = "active", "off" = "control"),
group = factor(group, levels = c("control", "active")))
predicted %>%
ggplot(aes(group, predicted, color = x, group = x)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.05), size = 1.5) +
geom_line(position = position_dodge(0.05), size = 1.5) +
scale_color_manual(name = "confidence rating", values = palette) +
scale_x_discrete(expand = c(.1, .1)) +
coord_cartesian(ylim = c(-.5, .65)) +
labs(y = "within-person craving rating (SD)\n", x = "\nintervention week") +
plot_aes +
theme(legend.position = "top")
summary
make_table(craving_week)| term | b [95% CI] |
|---|---|
| intercept | -0.07 [-0.18, 0.04] |
| intervention week (active) | 0.01 [-0.08, 0.11] |
| confidence_rating | -0.01 [-0.09, 0.06] |
| signal | 0.00 [-0.00, 0.01] |
| intervention week (active) x confidence_rating | 0.03 [-0.07, 0.13] |
simple slopes
hypothesis(craving_week,
c(expression_mean = "active_weekon = 0",
expression_1sd = "active_weekon + active_weekon:confidence_rating = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | 0.01 [-0.08, 0.11] |
| expression_1sd | 0.04 [-0.10, 0.19] |
drinking ~ active_week * confidence_rating
Do participants reporter drinking less (compared to their average) on active compared to control weeks?
Is this relationships stronger for participants who reported greater confidence with mindful attention?
plot
regression
vals = seq(-2, 2, .2)
predicted = ggeffects::ggpredict(drinking_week, c("confidence_rating [vals]", "active_week")) %>%
data.frame()
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_color_manual(name = "intervention week", values = palette) +
scale_fill_manual(name = "intervention week", values = palette) +
labs(y = "within-person number of drinks\n", x = "\nconfidence rating (SD)") +
plot_aes +
theme(legend.position = "top")
point range
predicted = ggeffects::ggpredict(drinking_week, c("confidence_rating [0, 1]", "active_week")) %>%
data.frame() %>%
mutate(x = ifelse(x == 1, "+1 SD", "mean"),
x = factor(x, levels = c("mean", "+1 SD")),
group = recode(group, "on" = "active", "off" = "control"),
group = factor(group, levels = c("control", "active")))
predicted %>%
ggplot(aes(group, predicted, color = x, group = x)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.05), size = 1.5) +
geom_line(position = position_dodge(0.05), size = 1.5) +
scale_color_manual(name = "confidence rating", values = palette) +
scale_x_discrete(expand = c(.1, .1)) +
coord_cartesian(ylim = c(-.3, .3)) +
labs(y = "within-person number of drinks\n", x = "\nintervention week") +
plot_aes +
theme(legend.position = "top")
summary
make_table(drinking_week)| term | b [95% CI] |
|---|---|
| intercept | 0.23 [0.08, 0.38] |
| intervention week (active) | -0.10 [-0.25, 0.06] |
| confidence_rating | 0.06 [-0.04, 0.16] |
| signal | -0.01 [-0.01, -0.00] |
| intervention week (active) x confidence_rating | -0.12 [-0.28, 0.03] |
simple slopes
hypothesis(drinking_week,
c(expression_mean = "active_weekon = 0",
expression_1sd = "active_weekon + active_weekon:confidence_rating = 0"))$hypothesis %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", Estimate, CI.Lower, CI.Upper)) %>%
select(Hypothesis, `b [95% CI]`) %>%
knitr::kable()| Hypothesis | b [95% CI] |
|---|---|
| expression_mean | -0.10 [-0.25, 0.06] |
| expression_1sd | -0.22 [-0.44, 0.00] |